Python 密集层的输入0与层不兼容：输入形状的预期轴-1的值为8192，但接收到形状的输入（无，61608）

我正在尝试创建一个图像处理CNN。我使用VGG16来加速一些学习过程。下面我的CNN的创建工作到了训练和保存模型和重量的程度。当我在加载模型后尝试运行预测函数时，就会出现这个问题

image_gen = ImageDataGenerator()
train = image_gen.flow_from_directory('./data/train', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
val = image_gen.flow_from_directory('./data/validate', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))

pretrained_model = VGG16(include_top=False, input_shape=(151, 136, 3), weights='imagenet')
pretrained_model.summary()

vgg_features_train = pretrained_model.predict(train)
vgg_features_val = pretrained_model.predict(val)

train_target = to_categorical(train.labels)
val_target = to_categorical(val.labels)

model = Sequential()
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='rmsprop', metrics=['accuracy'], loss='categorical_crossentropy')

target_dir = './models/weights-improvement'
if not os.path.exists(target_dir):
  os.mkdir(target_dir)

checkpoint = ModelCheckpoint(filepath=target_dir + 'weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

model.fit(vgg_features_train, train_target, epochs=100, batch_size=8, validation_data=(vgg_features_val, val_target), callbacks=callbacks_list)

model.save('./models/model')
model.save_weights('./models/weights')

我有一个预测函数，我想加载到一个图像中，然后返回模型给出的图像分类

from keras.preprocessing.image import load_img, img_to_array
def predict(file):
  x = load_img(file, target_size=(151,136,3))
  x = img_to_array(x)
  print(x.shape)
  print(x.shape)
  x = np.expand_dims(x, axis=0)
  array = model.predict(x)
  result = array[0]
  if result[0] > result[1]:
    if result[0] > 0.9:
      print("Predicted answer: Buy")
      answer = 'buy'
      print(result)
      print(array)
    else:
      print("Predicted answer: Not confident")
      answer = 'n/a'
      print(result)
  else:
    if result[1] > 0.9:
      print("Predicted answer: Sell")
      answer = 'sell'
      print(result)
    else:
      print("Predicted answer: Not confident")
      answer = 'n/a'
      print(result)

  return answer

我遇到的问题是，当我运行这个predict函数时，我得到以下错误

  File "predict-binary.py", line 24, in predict
    array = model.predict(x)
  File ".venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1629, in predict    
    tmp_batch_outputs = self.predict_function(iterator)
  File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 828, in __call__       
    result = self._call(*args, **kwds)
  File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 725, in _initialize    
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 3196, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File ".venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 634, in wrapped_fn     
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File ".venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 977, in wrapper      
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    .venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1478 predict_function  *       
        return step_function(self, iterator)
    .venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1468 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    .venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    .venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica 
        return self._call_for_each_replica(fn, args, kwargs)
    .venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    .venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1461 run_step  **
        outputs = model.predict_step(data)
    .venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1434 predict_step
        return self(x, training=False)
    .venv\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:1012 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    .venv\lib\site-packages\tensorflow\python\keras\engine\sequential.py:375 call
        return super(Sequential, self).call(inputs, training=training, mask=mask)
    .venv\lib\site-packages\tensorflow\python\keras\engine\functional.py:424 call
        return self._run_internal_graph(
    .venv\lib\site-packages\tensorflow\python\keras\engine\functional.py:560 _run_internal_graph      
        outputs = node.layer(*args, **kwargs)
    .venv\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    .venv\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:255 assert_input_compatibility
        raise ValueError(

    ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 8192 but received input with shape (None, 61608)

我假设我需要在模型的

flant（）

和

Dense（）

层之间进行一些更改，但我不确定是什么。我试图在这两者之间添加

model.add（Dense（61608，activation='relu））

，因为这似乎是我在另一篇文章中看到的建议（现在找不到链接），但它导致了相同的错误。（我用8192而不是61608试过了）。谢谢你的帮助

编辑#1：

更改模型创建/培训代码，正如我认为的那样

---

这导致model.fit（vgg_features_train，train_target，epochs=100，batch_size=8，validation_data=（vgg_features_val，val_target），callbacks=callbacks_list）中第37行的

文件“train binary.py”出现不同的输入形状错误值错误：输入0与层模型不兼容：预期形状=（无，151，136，3），找到形状=（无，512）

您的模型希望看到model.predict的输入，它的维度与培训时的维度相同。在这种情况下，它是vgg_特性的维度。model.predict的输入是为vgg模型的输入生成的。您基本上是在尝试进行迁移学习，因此我建议您按照以下步骤进行操作

base_model=tf.keras.applications.VGG19( include_top=False, input_shape=img_shape, pooling='max', weights='imagenet' )
x=base_model.output
x=Dense(100, activation='relu'))(x)
x=Dropout(0.5)(x)
x=BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)
output=Dense(2, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.fit( train, epochs=100, batch_size=8, validation_data=val, callbacks=callbacks_list)

---

现在，对于预测，您可以使用与训练模型相同的尺寸。

模型训练成功，对吗？@yudhiesh是的，训练已完成，使用的图像与训练过程中使用的“/data/validate”文件夹相同。我是如何得到错误的，我认为我不再需要

train\u目标的。

和

val_target

变量？不，现在只需将训练和验证生成器直接输入model.fit。请参阅修改后的回答我按照我认为您建议的方式更改了代码，但这之后寻找了与我在

input_shape

中提供的不同的输入形状。如果您能看到我所做的，我在帖子中添加了代码错了。谢谢你的帮助。我应该补充一点，这个错误发生在现在的模型训练中，而不是像以前那样的预测中

image_gen = ImageDataGenerator()
train = image_gen.flow_from_directory('./data/train', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
val = image_gen.flow_from_directory('./data/validate', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))

pretrained_model = VGG16(include_top=False, input_shape=(151, 136, 3), weights='imagenet')
pretrained_model.summary()

vgg_features_train = pretrained_model.predict(train)
vgg_features_val = pretrained_model.predict(val)

train_target = to_categorical(train.labels)
val_target = to_categorical(val.labels)

model = Sequential()
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='rmsprop', metrics=['accuracy'], loss='categorical_crossentropy')

target_dir = './models/weights-improvement'
if not os.path.exists(target_dir):
  os.mkdir(target_dir)

checkpoint = ModelCheckpoint(filepath=target_dir + 'weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

model.fit(vgg_features_train, train_target, epochs=100, batch_size=8, validation_data=(vgg_features_val, val_target), callbacks=callbacks_list)

model.save('./models/model')
model.save_weights('./models/weights')